The present disclosure relates generally to vehicle object detection systems and, more particularly, to a method and system for reducing repeat false alarm indications in vehicle impact detection systems through position learning algorithms that maintain an editable database of false alarm object positions.
One of the more recent systems to be developed in the automotive industry is a collision warning system (CWS). A CWS is intended to mitigate and/or eliminate vehicle impacts by generating a timely warning to the driver to take an evasive action. Typically, a vehicle is configured with a sensor (or sensors) that is capable of detecting objects in the frontal area of the vehicle. The sensor not only detects the presence of an object, but also provides some quantitative information about the object such as range, range rate, and azimuth position of the object. Additional information related to the object (e.g., a lead vehicle in many instances) may include relative acceleration, the size of the object, the dimensions of the object, the direction of movement of the object, etc. Generally speaking, two main technologies are most prevalent in gathering such object information: (1) laser technology; and (2) radar technology.
In addition to the gathered object data, a CWS also typically incorporates a path prediction algorithm and a threat assessment algorithm, which evaluate the incoming data, analyze the particular situation, and then determine if there is any imminent threat of impacting an object in the frontal area of the vehicle. Many of these algorithms are based on parameters such as “time to impact”, “time headway”, or perhaps basic vehicle kinematics. In any case, a determined threat level above a given threshold will cause the CWS to issue a warning to the driver.
False alarms generated from a collision warning system are a source of nuisance to the driver. Such false alarms may result from erroneous information picked up from one or more of the sensors, or may be generated as the result of shortcomings in the threat assessment algorithm or path prediction algorithms themselves. Statistical measures, such as number of false alarms per mile driven or number of false alarms per hour of driving, are commonly used to measure the effectiveness of collision warning systems. Although previous efforts have been focused upon improving sensors, path prediction algorithms, threat assessment algorithms and the like to reduce the rate of false alarms, relatively little effort has been spent in reducing the number of false alarms resulting from a specific roadway property or object.
More specifically, it has been discovered that an individual roadway property or object has the ability to cause a false alarm each time the vehicle encounters the object. Accordingly, it would be desirable for a collision warning system to have the ability learn about specific properties or objects that lead to false alarms, and thereafter adjust the properties of the warning system algorithms appropriately. Thereby, a lower false alarm rate may be achieved.